The numbers of cases and deaths due to the COVID-19 virus have increased daily all around the world.Chest X-ray is considered very useful and less time-consuming for monitoring COVID disease.No doubt,X-ray is consider...The numbers of cases and deaths due to the COVID-19 virus have increased daily all around the world.Chest X-ray is considered very useful and less time-consuming for monitoring COVID disease.No doubt,X-ray is considered as a quick screening method,but due to variations in features of images which are of X-rays category with Corona confirmed cases,the domain expert is needed.To address this issue,we proposed to utilize deep learning approaches.In this study,the dataset of COVID-19,lung opacity,viral pneumonia,and lastly healthy patients’images of category X-rays are utilized to evaluate the performance of the Swin transformer for predicting the COVID-19 patients efficiently.The performance of the Swin transformer is compared with the other seven deep learning models,including ResNet50,DenseNet121,InceptionV3,EfficientNetB2,VGG19,ViT,CaIT,Swim transformer provides 98%recall and 96%accuracy on corona affected images of the X-ray category.The proposed approach is also compared with state-of-the-art techniques for COVID-19 diagnosis,and proposed technique is found better in terms of accuracy.Our system could support clin-icians in screening patients for COVID-19,thus facilitating instantaneous treatment for better effects on the health of COVID-19 patients.Also,this paper can contribute to saving humanity from the adverse effects of trials that the Corona virus might bring by performing an accurate diagnosis over Corona-affected patients.展开更多
Since reporting cases of breast cancer are on the rise all over the world.Especially in regions such as Pakistan,Saudi Arabia,and the United States.Efficient methods for the early detection and diagnosis of breast can...Since reporting cases of breast cancer are on the rise all over the world.Especially in regions such as Pakistan,Saudi Arabia,and the United States.Efficient methods for the early detection and diagnosis of breast cancer are needed.The usual diagnosis procedures followed by physicians has been updated with modern diagnostic approaches that include computer-aided support for better accuracy.Machine learning based practices has increased the accuracy and efficiency of medical diagnosis,which has helped save lives of many patients.There is much research in the field of medical imaging diagnostics that can be applied to the variety of data such as magnetic resonance images(MRIs),mammograms,X-rays,ultrasounds,and histopathological images,but magnetic resonance(MR)and mammogram imaging have proved to present the promising results.The proposed paper has presented the results of classification algorithms over Breast Cancer(BC)mammograms from a novel dataset taken from hospitals in the Qassim health cluster of Saudi Arabia.This paper has developed a novel approach called the novel spectral extraction algorithm(NSEA)that uses feature extraction and fusion by using local binary pattern(LBP)and bilateral algorithms,as well as a support vector machine(SVM)as a classifier.The NSEA with the SVM classifier demonstrated a promising accuracy of 94%and an elapsed time of 0.68 milliseconds,which were significantly better results than those of comparative experiments from classifiers named Naïve Bayes,logistic regression,K-Nearest Neighbor(KNN),Gaussian Discriminant Analysis(GDA),AdaBoost and Extreme Learning Machine(ELM).ELM produced the comparative accuracy of 94%however has a lower elapsed time of 1.35 as compared to SVM.Adaboost has produced a fairly well accuracy of 82%,KNN has a low accuracy of 66%.However Logistic Regression,GDA and Naïve Bayes have produced the lowest accuracies of 47%,43%and 42%.展开更多
Breast cancer(BC)is the most common cause of women’s deaths worldwide.The mammography technique is the most important modality for the detection of BC.To detect abnormalities in mammographic images,the Breast Imaging...Breast cancer(BC)is the most common cause of women’s deaths worldwide.The mammography technique is the most important modality for the detection of BC.To detect abnormalities in mammographic images,the Breast Imaging Reporting and Data System(BI-RADs)is used as a baseline.The correct allocation of BI-RADs categories for mammographic images is always an interesting task,even for specialists.In this work,to detect and classify the mammogram images in BI-RADs,a novel hybrid model is presented using a convolutional neural network(CNN)with the integration of a support vector machine(SVM).The dataset used in this research was collected from different hospitals in the Qassim health cluster of Saudi Arabia.The collection of all categories of BI-RADs is one of the major contributions of this paper.Another significant contribution is the development of a hybrid approach through the integration of CNN and SVM.The proposed hybrid approach uses three CNN models to obtain ensemble CNN model results.This ensemble model saves the values to integrate them with SVM.The proposed system achieved a classification accuracy,sensitivity,specificity,precision,and F1-score of 93.6%,94.8%,96.9%,96.6%,and 95.7%,respectively.The proposed model achieved better performance compared to previously available methods.展开更多
基金funded by the Deanship of Scientific Research,Najran University,Kingdom of Saudi Arabia,Grant Number NU/MID/18/035.
文摘The numbers of cases and deaths due to the COVID-19 virus have increased daily all around the world.Chest X-ray is considered very useful and less time-consuming for monitoring COVID disease.No doubt,X-ray is considered as a quick screening method,but due to variations in features of images which are of X-rays category with Corona confirmed cases,the domain expert is needed.To address this issue,we proposed to utilize deep learning approaches.In this study,the dataset of COVID-19,lung opacity,viral pneumonia,and lastly healthy patients’images of category X-rays are utilized to evaluate the performance of the Swin transformer for predicting the COVID-19 patients efficiently.The performance of the Swin transformer is compared with the other seven deep learning models,including ResNet50,DenseNet121,InceptionV3,EfficientNetB2,VGG19,ViT,CaIT,Swim transformer provides 98%recall and 96%accuracy on corona affected images of the X-ray category.The proposed approach is also compared with state-of-the-art techniques for COVID-19 diagnosis,and proposed technique is found better in terms of accuracy.Our system could support clin-icians in screening patients for COVID-19,thus facilitating instantaneous treatment for better effects on the health of COVID-19 patients.Also,this paper can contribute to saving humanity from the adverse effects of trials that the Corona virus might bring by performing an accurate diagnosis over Corona-affected patients.
基金The authors would like to acknowledge the support of the Deputy for Research and Innovation—Ministry of Education,Kingdom of Saudi Arabia for funding this research through a project grant code(NU/IFC/ENT/01/009)under the institutional Funding Committee at Najran University,Kingdom of Saudi Arabia.
文摘Since reporting cases of breast cancer are on the rise all over the world.Especially in regions such as Pakistan,Saudi Arabia,and the United States.Efficient methods for the early detection and diagnosis of breast cancer are needed.The usual diagnosis procedures followed by physicians has been updated with modern diagnostic approaches that include computer-aided support for better accuracy.Machine learning based practices has increased the accuracy and efficiency of medical diagnosis,which has helped save lives of many patients.There is much research in the field of medical imaging diagnostics that can be applied to the variety of data such as magnetic resonance images(MRIs),mammograms,X-rays,ultrasounds,and histopathological images,but magnetic resonance(MR)and mammogram imaging have proved to present the promising results.The proposed paper has presented the results of classification algorithms over Breast Cancer(BC)mammograms from a novel dataset taken from hospitals in the Qassim health cluster of Saudi Arabia.This paper has developed a novel approach called the novel spectral extraction algorithm(NSEA)that uses feature extraction and fusion by using local binary pattern(LBP)and bilateral algorithms,as well as a support vector machine(SVM)as a classifier.The NSEA with the SVM classifier demonstrated a promising accuracy of 94%and an elapsed time of 0.68 milliseconds,which were significantly better results than those of comparative experiments from classifiers named Naïve Bayes,logistic regression,K-Nearest Neighbor(KNN),Gaussian Discriminant Analysis(GDA),AdaBoost and Extreme Learning Machine(ELM).ELM produced the comparative accuracy of 94%however has a lower elapsed time of 1.35 as compared to SVM.Adaboost has produced a fairly well accuracy of 82%,KNN has a low accuracy of 66%.However Logistic Regression,GDA and Naïve Bayes have produced the lowest accuracies of 47%,43%and 42%.
基金Authors would like to acknowledge the support of the Deputy for Research and Innovation-Ministry of Education,Kingdom of Saudi Arabia for this research through a project grant(NU/IFC/ENT/01/009)under the institutional Funding Committee at Najran University,Kingdom of Saudi Arabia.
文摘Breast cancer(BC)is the most common cause of women’s deaths worldwide.The mammography technique is the most important modality for the detection of BC.To detect abnormalities in mammographic images,the Breast Imaging Reporting and Data System(BI-RADs)is used as a baseline.The correct allocation of BI-RADs categories for mammographic images is always an interesting task,even for specialists.In this work,to detect and classify the mammogram images in BI-RADs,a novel hybrid model is presented using a convolutional neural network(CNN)with the integration of a support vector machine(SVM).The dataset used in this research was collected from different hospitals in the Qassim health cluster of Saudi Arabia.The collection of all categories of BI-RADs is one of the major contributions of this paper.Another significant contribution is the development of a hybrid approach through the integration of CNN and SVM.The proposed hybrid approach uses three CNN models to obtain ensemble CNN model results.This ensemble model saves the values to integrate them with SVM.The proposed system achieved a classification accuracy,sensitivity,specificity,precision,and F1-score of 93.6%,94.8%,96.9%,96.6%,and 95.7%,respectively.The proposed model achieved better performance compared to previously available methods.
